feat: Add functionality to only benchmark FX

- Add support for FX in YAML config specification path of `perf_run.py`
- Add support for only specifying one of: a PyTorch or TorchScript model
- Improve input model validation to throw errors earlier
- Remove deprecated option from torchvision model

Author: gs-olive

tools/perf/README.md

@@ -66,9 +66,10 @@ There are two sample configuration files added.
 
 | Name              | Supported Values                     | Description                                                  |
 | ----------------- | ------------------------------------ | ------------------------------------------------------------ |
-| backend           | all, torch, torch_tensorrt, tensorrt | Supported backends for inference.                            |
+| backend           | all, torch, torch_tensorrt, tensorrt, fx2trt | Supported backends for inference.                            |
 | input             | -                                    | Input binding names. Expected to list shapes of each input bindings |
 | model             | -                                    | Configure the model filename and name                        |
+| model_torch             | -                              | Name of torch model file and name (used for fx2trt) (optional)                  |
 | filename          | -                                    | Model file name to load from disk.                           |
 | name              | -                                    | Model name                                                   |
 | runtime           | -                                    | Runtime configurations                                       |
@@ -83,6 +84,7 @@ backend:
   - torch
   - torch_tensorrt
   - tensorrt
+  - fx2trt
 input:
   input0:
     - 3
@@ -92,6 +94,9 @@ input:
 model:
   filename: model.plan
   name: vgg16
+model_torch:
+  filename: model_torch.pt
+  name: vgg16
 runtime:
   device: 0
   precision:
@@ -108,8 +113,9 @@ Note:
 
 Here are the list of `CompileSpec` options that can be provided directly to compile the pytorch module
 
-* `--backends` : Comma separated string of backends. Eg: torch,torch_tensorrt, tensorrt or fx2trt
+* `--backends` : Comma separated string of backends. Eg: torch, torch_tensorrt, tensorrt or fx2trt
 * `--model` : Name of the model file (Can be a torchscript module or a tensorrt engine (ending in `.plan` extension)). If the backend is `fx2trt`, the input should be a Pytorch module (instead of a torchscript module) and the options for model are (`vgg16` | `resnet50` | `efficientnet_b0`)
+* `--model_torch` : Name of the PyTorch model file (optional, only necessary if fx2trt is a chosen backend)
 * `--inputs` : List of input shapes & dtypes. Eg: (1, 3, 224, 224)@fp32 for Resnet or (1, 128)@int32;(1, 128)@int32 for BERT
 * `--batch_size` : Batch size
 * `--precision` : Comma separated list of precisions to build TensorRT engine Eg: fp32,fp16
@@ -122,9 +128,10 @@ Eg:
 
 ```
   python perf_run.py --model ${MODELS_DIR}/vgg16_scripted.jit.pt \
+                     --model_torch ${MODELS_DIR}/vgg16_torch.pt \
                      --precision fp32,fp16 --inputs="(1, 3, 224, 224)@fp32" \
                      --batch_size 1 \
-                     --backends torch,torch_tensorrt,tensorrt \
+                     --backends torch,torch_tensorrt,tensorrt,fx2trt \
                      --report "vgg_perf_bs1.txt"
 ```
 

tools/perf/hub.py

@@ -26,7 +26,10 @@
 
 # Key models selected for benchmarking with their respective paths
 BENCHMARK_MODELS = {
-    "vgg16": {"model": models.vgg16(pretrained=True), "path": ["script", "pytorch"]},
+    "vgg16": {
+        "model": models.vgg16(weights=models.VGG16_Weights.DEFAULT),
+        "path": ["script", "pytorch"],
+    },
     "resnet50": {
         "model": models.resnet50(weights=None),
         "path": ["script", "pytorch"],

tools/perf/perf_run.py

@@ -292,6 +292,13 @@ def run(
                 print("int8 precision expects calibration cache file for inference")
                 return False
 
+        if (model is None) and (backend != "fx2trt"):
+            warnings.warn(
+                f"Requested backend {backend} without specifying a TorchScript Model, "
+                + "skipping this backend"
+            )
+            continue
+
         if backend == "all":
             run_torch(model, input_tensors, params, precision, batch_size)
             run_torch_tensorrt(
@@ -371,9 +378,14 @@ def recordStats(backend, timings, precision, batch_size=1, compile_time_ms=None)
     results.append(stats)
 
 
-def load_model(params):
+def load_ts_model(params):
     model = None
     is_trt_engine = False
+
+    # No TorchScript Model Specified
+    if len(params.get("model", "")) == 0:
+        return None, None, is_trt_engine
+
     # Load torch model traced/scripted
     model_file = params.get("model").get("filename")
     try:
@@ -393,6 +405,26 @@ def load_model(params):
     return model, model_name, is_trt_engine
 
 
+def load_torch_model(params):
+    model = None
+
+    # No Torch Model Specified
+    if len(params.get("model_torch", "")) == 0:
+        return None, None
+
+    # Load torch model
+    model_file = params.get("model_torch").get("filename")
+    try:
+        model_name = params.get("model_torch").get("name")
+    except:
+        model_name = model_file
+
+    print("Loading Torch model: ", model_file)
+    model = torch.load(model_file).cuda()
+
+    return model, model_name
+
+
 if __name__ == "__main__":
     arg_parser = argparse.ArgumentParser(
         description="Run inference on a model with random input values"
@@ -408,7 +440,9 @@ def load_model(params):
         type=str,
         help="Comma separated string of backends. Eg: torch,torch_tensorrt,fx2trt,tensorrt",
     )
-    arg_parser.add_argument("--model", type=str, help="Name of torchscript model file")
+    arg_parser.add_argument(
+        "--model", type=str, default="", help="Name of torchscript model file"
+    )
     arg_parser.add_argument(
         "--model_torch",
         type=str,
@@ -458,7 +492,16 @@ def load_model(params):
         parser = ConfigParser(args.config)
         # Load YAML params
         params = parser.read_config()
-        model, model_name, is_trt_engine = load_model(params)
+        model, model_name, is_trt_engine = load_ts_model(params)
+        model_torch, model_name_torch = load_torch_model(params)
+
+        # If neither model type was provided
+        if (model is None) and (model_torch is None):
+            raise ValueError(
+                "No valid models specified. Please provide a torchscript model file or model name "
+                + "(among the following options vgg16|resnet50|efficientnet_b0|vit) "
+                + "or provide a torch model file"
+            )
 
         # Default device is set to 0. Configurable using yaml config file.
         torch.cuda.set_device(params.get("runtime").get("device", 0))
@@ -489,7 +532,10 @@ def load_model(params):
 
             if not is_trt_engine and (precision == "fp16" or precision == "half"):
                 # If model is TensorRT serialized engine then model.half will report failure
-                model = model.half()
+                if model is not None:
+                    model = model.half()
+                if model_torch is not None:
+                    model_torch = model_torch.half()
 
             backends = params.get("backend")
             # Run inference
@@ -502,6 +548,7 @@ def load_model(params):
                 truncate_long_and_double,
                 batch_size,
                 is_trt_engine,
+                model_torch,
             )
     else:
         params = vars(args)
@@ -511,23 +558,27 @@ def load_model(params):
         model_name_torch = params["model_torch"]
         model_torch = None
 
-        # Load TorchScript model
+        # Load TorchScript model, if provided
         if os.path.exists(model_name):
             print("Loading user provided torchscript model: ", model_name)
             model = torch.jit.load(model_name).cuda().eval()
         elif model_name in BENCHMARK_MODELS:
             print("Loading torchscript model from BENCHMARK_MODELS for: ", model_name)
             model = BENCHMARK_MODELS[model_name]["model"].eval().cuda()
-        else:
-            raise ValueError(
-                "Invalid model name. Please provide a torchscript model file or model name (among the following options vgg16|resnet50|efficientnet_b0|vit)"
-            )
 
         # Load PyTorch Model, if provided
         if len(model_name_torch) > 0 and os.path.exists(model_name_torch):
             print("Loading user provided torch model: ", model_name_torch)
             model_torch = torch.load(model_name_torch).eval().cuda()
 
+        # If neither model type was provided
+        if (model is None) and (model_torch is None):
+            raise ValueError(
+                "No valid models specified. Please provide a torchscript model file or model name "
+                + "(among the following options vgg16|resnet50|efficientnet_b0|vit) "
+                + "or provide a torch model file"
+            )
+
         backends = parse_backends(params["backends"])
         truncate_long_and_double = params["truncate"]
         batch_size = params["batch_size"]

tools/perf/utils.py

@@ -5,7 +5,10 @@
 import timm
 
 BENCHMARK_MODELS = {
-    "vgg16": {"model": models.vgg16(pretrained=True), "path": ["script", "pytorch"]},
+    "vgg16": {
+        "model": models.vgg16(weights=models.VGG16_Weights.DEFAULT),
+        "path": ["script", "pytorch"],
+    },
     "resnet50": {
         "model": models.resnet50(weights=None),
         "path": ["script", "pytorch"],